12. Cartographie: aller plus loin
Simon Gabay
19/10/2021
0. Préparatifs
setwd("~/GitHub/UNIGE/32M7129/Cours_12")
#je charge les données que l'enseignant a préparé pour éviter de potentiels problèmes
load("Cours_Geneve_12.RData")if(!require("leaflet")){
install.packages("leaflet")
library(leaflet)
}
if(!require("rgdal")){
install.packages("rgdal")
library(rgdal)
}
if(!require("sf")){
install.packages("sf")
library(sf)
}
if(!require("ggplot2")){
install.packages("ggplot2")
library(ggplot2)
}
if(!require("raster")){
install.packages("raster")
library(raster)
}
if(!require("plyr")){
install.packages("plyr")
library(plyr)
}
if(!require("leaflet.extras")){
install.packages("leaflet.extras")
library(leaflet.extras)
}
if(!require("htmlwidgets")){
install.packages("htmlwidgets")
library(htmlwidgets)
}
if(!require("geojsonio")){
install.packages("geojsonio")
library(geojsonio)
}
if(!require("jsonlite")){
install.packages("jsonlite")
library(jsonlite)
}
if(!require("leaftime")){
install.packages("leaftime")
library(leaftime)
}1. Travailler avec des shapefiles
On le rappelle, les fichiers .shp sont aussi des fichiers complexes, qui contiennent des informations en plus des simples formes géométriques. En l’occurrence, notre document contient
## area occurrences
## 1 Arménie 2
## 2 Allemagne 7
## 3 France 24
## 4 Grande-Bretagne 2
## 5 Grèce 84
## 6 Islande 1
## 7 Italie 51
## 8 Russie 1
## 9 Serbie 1
## 10 Anatolie 14
## 11 Levant 9
## 12 Transylvanie 1
## 13 Afrique romaine 9
## 14 Extrême-Orient 6
## 15 Péninsule Ibérique 49
## 16 Arabie 2
## 17 Amériques 3
## 18 Orient 19On crée une palette, qui utilise une palette de couleur prédéfinie et qui est fonction du nombre d’occurrence par zone
leaflet(shp)%>%
## On ajoute un fond de carte
addProviderTiles(providers$Esri.NatGeoWorldMap)%>%
# Je définis le point central de ma carte
setView(lng = 13,
lat = 40,
zoom = 2.2 )%>%
# J'ajoute mes grandes zones
addPolygons(data = shp,
stroke = FALSE,
smoothFactor = 0.2,
color = ~pal(shp$number),
group = "Zones",
fillOpacity = 1)preserve13e8182c7668a2d9
Je peux évidemment mélanger les sources
#import du CSV de Boyer (cf. cours précédent)
boyer <- read.csv("Data/boyer.csv")
leaflet(shp)%>%
## On ajoute un fond de carte
addProviderTiles(providers$Esri.NatGeoWorldMap)%>%
# Je définis le point central de ma carte
setView(lng = 13,
lat = 40,
zoom = 2.2 )%>%
# J'ajoute mes grandes zones
addPolygons(data = shp,
stroke = FALSE,
smoothFactor = 0.2,
color = ~pal(shp$number),
group = "Zones",
fillOpacity = 1)%>%
# J'ajoute les points issues des pièces de Boyer, importées précédemment
addCircleMarkers(lng = boyer$lng,
lat = boyer$lat,
color = "brown",
group = "Boyer",
weight = 1,
radius = 14,
fillOpacity = 0.6)preservebbef15a3c47caba0
Enfin, un petit coup de tuning
100%
map<-leaflet(shp)%>%
## On ajoute un fond de carte
addProviderTiles(providers$Esri.NatGeoWorldMap)%>%
# Je définis le point central de ma carte
setView(lng = 13,
lat = 40,
zoom = 2.2 )%>%
# J'ajoute mes grandes zones
addPolygons(data = shp,
stroke = FALSE,
smoothFactor = 0.2,
color = ~pal(shp$number),
group = "Zones",
fillOpacity = 1)%>%
# J'ajoute les points issues des pièces de Boyer, importées précédemment
addCircleMarkers(lng = boyer$lng,
lat = boyer$lat,
color = "brown",
group = "Boyer",
weight = 1,
radius = 14,
fillOpacity = 0.6) %>%
#J'ajoute une fonction "reset", qui permet de reactiver setView()
addResetMapButton() %>%
#J'ajoute un panneau de contrôle qui me permet de choisir ce que je sélectionne
addLayersControl(overlayGroups = c("Zones", "Boyer"),
options = layersControlOptions(collapsed = TRUE)) %>%
#Je cache par défaut les points tirés des pièces de Boyer
hideGroup(c("Boyer"))
mappreserve6e7df525d997e8c5
Je peux sauvegarder ma carte en html et l’ouvrir dans le navigateur (attention, cela peut prendre du temps).
2. Timeline
Nous reprenons le même jeu de données que précédemment, avec nos quatre villes.
sales <- data.frame(
"Latitude" = c(
48.8534, 52.520008, 51.509865, 50.8466
),
"Longitude" = c(
2.3488, 13.404954, -0.118092, 4.3528
),
"population" = c(
2.148, 3.769,8.982, 1.209 # en millions
),
labels = c("Paris", "Berlin", "Londres", "Bruxelles")
)Pour créer une Timeline il va logiquement falloir associer des dates: une de début, et une de fin.
#j'ajoute une colonne à mon dataframe
sales$start <- c(
"start" = do.call(
#je précise que les données sont des dates
"as.Date",
#je crée une liste avec
list(
#les données
x = c("1-Jan-1918", "1-Jan-1919","1-Jan-1920", "1-Jan-1921"),
#le format des données
format = "%d-%b-%Y"
)
)
)
#je recommence la même manipulation, cette fois avec la date de fin
sales$end <- c(
"end" = do.call(
"as.Date",
list(
x = c("30-Dec-1918", "30-Dec-1919","30-Dec-1920", "30-Dec-1921"),
format = "%d-%b-%Y"
)
)
)
#Je pourrais copier-coller les dates de début si ce sont les mêmes que les dates de fin
#sales$end<-sales$startJ’obtiens donc un data frame, avec la population, les dates de début, les dates de fin
## Latitude Longitude population labels start end
## 1 48.85340 2.348800 2.148 Paris 1918-01-01 1918-12-30
## 2 52.52001 13.404954 3.769 Berlin 1919-01-01 1919-12-30
## 3 51.50986 -0.118092 8.982 Londres 1920-01-01 1920-12-30
## 4 50.84660 4.352800 1.209 Bruxelles 1921-01-01 1921-12-30Je peux désormais convertir mon dataframe en fichier GEOjson:
## Registered S3 method overwritten by 'geojsonlint':
## method from
## print.location dplyrLe fichier GEOjson que nous avons fabriqué respecte les recommendations de Leaflet.timeline, qui est un type spécifique de fichier GEOjson. Le début est le suivant:
{"type":
"FeatureCollection",
"features":
[
{"type":"Feature",
"geometry": {
"type":"Point",
"coordinates":[2.3488,48.8534]
},
"properties":{
"labels":"Paris",
"population":"2.148",
"start":"1918-01-01",
"end":"1918-01-01"
}
},
{"type":"Feature",
"geometry":{
"type":"Point",
"coordinates":[13.404954,52.52001]
},
"properties":{
"labels":"Berlin",
"population":"3.769",
"start":"1919-01-01",
"end":"1919-01-01"
}
},
…Je peux sauvegarder le résultat au format json si je veux regarder en détail le code produit:
Je peux désormais créer ma première carte avec timeline.
leaflet(sales_timeline) %>%
addTiles() %>%
setView(lng = 5, lat = 50, zoom = 5) %>%
# il suffit d'ajouter la fonction addTimeline(), c'est tout
addTimeline()preservec52f2e59c80a6a89
Je peux néanmoins faire mieux, et ajouter des options. Jouez un peu avec celles-ci pour apprendre à les maîtriser.
leaflet(sales_timeline) %>%
addTiles() %>%
setView(lng = 5, lat = 50, zoom = 5) %>%
addTimeline(
#largeur du bouton de contrôle
width = "50%",
sliderOpts = sliderOptions(
#position sur la carte de la timeline
position = "topright", #bottomright…
#nombre d'étapes lors du défilement de la timeline
step = 20,
#rapidité du défilement
duration = 4000
)
)preserve170778c5d392d4d0
Je peux aussi modifier la forme des marqueurs (marker) que j’utilise sur ma carte.
leaflet(sales_timeline) %>%
addTiles() %>%
setView(lng = 5, lat = 50, zoom = 5) %>%
addTimeline(
#largeur du bouton de contrôle
width = "50%",
sliderOpts = sliderOptions(
#position sur la carte de la timeline
position = "topright", #bottomright…
#nombre d'étapes lors du défilement de la timeline
step = 20,
#rapidité du défilement
duration = 4000
),
timelineOpts = timelineOptions(
styleOptions = styleOptions(
#rayon des marqueurs (qui seront donc des cercles)
radius = 10,
#couleur de la bordure des marqueurs
color = "grey",
#couleur de fond des marqueurs
fillColor = "orange",
#opacité du marqueur
fillOpacity = 1
)
)
)preservedbddbfee42359ecf
Je crée une copie de mon data frame, et je crée une colonne radius (rayon) qui est fonction de la population de la ville
J’utilise cette information pour gérer la taille des marqueurs
leaflet(geojson_json(sales_radius)) %>%
addTiles() %>%
setView(lng = 5, lat = 50, zoom = 5) %>%
addTimeline(
#largeur du bouton de contrôle
width = "50%",
sliderOpts = sliderOptions(
#position sur la carte de la timeline
position = "topright", #bottomright…
#nombre d'étapes lors du défilement de la timeline
step = 20,
#rapidité du défilement
duration = 4000
),
timelineOpts = timelineOptions(
# j'annule tous les styles (et j'évite que les paramètres par défaut ne soient utilisés)
styleOptions = NULL,
#Je vais mettre mes options de visualisation ici
pointToLayer = htmlwidgets::JS(
"
function(data, latlng) {
return L.circleMarker(
latlng,
{
radius: +data.properties.radius,
fillOpacity: 1
}
);
}
"
)
)
)## Assuming 'Longitude' and 'Latitude' are longitude and latitude, respectivelypreserve1a617a64dd3b31ed
3. Carte statique
3.1 Des rasters
Un autre approche de la cartographie va être de produire des cartes statiques. Nous allons, pour ce faire, utiliser des rasters (et donc le package du même nom).
Il est ensuite très simple d’afficher ce fond de carte
On aura remarqué le paramètre level=0. Chacun des niveaux représente une strate administrative
Evidemment, ce système fonctionne avec d’autres pays:
Rappelons-nous que ces coordonnées sont géographiques et non cartographiques: je dois donc les transformer avec la fonction spTransform(). J’utilise ici le système géodésique WGS 84 (World Geodetic System 1984) et j’obtiens donc des données UTM (Universal Transverse Mercator).
## class : SpatialPolygonsDataFrame
## features : 26
## extent : 5.956063, 10.49511, 45.81706, 47.80848 (xmin, xmax, ymin, ymax)
## crs : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
## variables : 10
## names : GID_0, NAME_0, GID_1, NAME_1, VARNAME_1, NL_NAME_1, TYPE_1, ENGTYPE_1, CC_1, HASC_1
## min values : CHE, Switzerland, CHE.1_1, Aargau, Appenzell Ausser-Rhoden|Appenzell Outer Rhodes|Appenzell dadens|Appenzell Rhodes Extérieures|Appenzello Esterno, NA, Canton|Kanton|Chantun, Canton, NA, CH.AG
## max values : CHE, Switzerland, CHE.9_1, Zürich, Zoug|Zugo, NA, Canton|Kanton|Chantun, Canton, NA, CH.ZH## GID_0 NAME_0 GID_1 NAME_1
## 1 CHE Switzerland CHE.1_1 Aargau
## 12 CHE Switzerland CHE.2_1 Appenzell Ausserrhoden
## 20 CHE Switzerland CHE.3_1 Appenzell Innerrhoden
## 21 CHE Switzerland CHE.4_1 Basel-Landschaft
## 22 CHE Switzerland CHE.5_1 Basel-Stadt
## 23 CHE Switzerland CHE.6_1 Bern
## 24 CHE Switzerland CHE.7_1 Fribourg
## 25 CHE Switzerland CHE.8_1 Genève
## 26 CHE Switzerland CHE.9_1 Glarus
## 2 CHE Switzerland CHE.10_1 Graubünden
## 3 CHE Switzerland CHE.11_1 Jura
## 4 CHE Switzerland CHE.12_1 Lucerne
## 5 CHE Switzerland CHE.13_1 Neuchâtel
## 6 CHE Switzerland CHE.14_1 Nidwalden
## 7 CHE Switzerland CHE.15_1 Obwalden
## 8 CHE Switzerland CHE.16_1 Sankt Gallen
## 9 CHE Switzerland CHE.17_1 Schaffhausen
## 10 CHE Switzerland CHE.18_1 Schwyz
## 11 CHE Switzerland CHE.19_1 Solothurn
## 13 CHE Switzerland CHE.20_1 Thurgau
## 14 CHE Switzerland CHE.21_1 Ticino
## 15 CHE Switzerland CHE.22_1 Uri
## 16 CHE Switzerland CHE.23_1 Valais
## 17 CHE Switzerland CHE.24_1 Vaud
## 18 CHE Switzerland CHE.25_1 Zug
## 19 CHE Switzerland CHE.26_1 Zürich
## VARNAME_1
## 1 Argovia|Arg¢via|Argovie
## 12 Appenzell Ausser-Rhoden|Appenzell Outer Rhodes|Appenzell dadens|Appenzell Rhodes Extérieures|Appenzello Esterno
## 20 Appenzell Inner-Rhoden|Appenzell Inner Rhodes|Appenzell dador|Appenzell Rhodes Intérieures|Appenzello Interno
## 21 Bâle-Campagne|Basel-Country|Baselland|Basel-Land|Basilea campagna|Basilea Campaña|Basilea-Champagna
## 22 Bâle-Ville|Basel-City|Basel-Town|Basilea-Citad|Basilea Ciudad|Basilea città |Basiléia cidade
## 23 Berna|Berne
## 24 Freiburg|Friburg|Friburgo
## 25 Cenevre|Genebra|Geneve|Geneva|Genevra|Genf|Ginebra|Ginevra
## 26 Glaris|Glarona|Glaruna
## 2 Graubünden|Grigioni|Grischun|Grisons
## 3 Giura
## 4 Lucerna|Luzern
## 5 Neuenburg
## 6 Nidvaldo|Nidwald|Unterwalden-le-Bas|Nidwaldo
## 7 Obvaldo|Obwald|Unterwalden-le-Haut|Obwaldo|Sursilvania
## 8 Saint-Gall|San Gallo|Son Gagl
## 9 Schaffhouse|Schaffusa|Sciaffusa
## 10 <NA>
## 11 Soletta|Soleure|Soleuro|Soloturn
## 13 Thurgovie|Turgovia|Turg¢via
## 14 Tesino|Tessin
## 15 <NA>
## 16 Vallais|Vallese|Wallis
## 17 Vad|Waadt|Waadtland
## 18 Zoug|Zugo
## 19 Turitg|Zurigo|Zürih|Zurique
## NL_NAME_1 TYPE_1 ENGTYPE_1 CC_1 HASC_1
## 1 <NA> Canton|Kanton|Chantun Canton <NA> CH.AG
## 12 <NA> Canton|Kanton|Chantun Canton <NA> CH.AR
## 20 <NA> Canton|Kanton|Chantun Canton <NA> CH.AI
## 21 <NA> Canton|Kanton|Chantun Canton <NA> CH.BL
## 22 <NA> Canton|Kanton|Chantun Canton <NA> CH.BS
## 23 <NA> Canton|Kanton|Chantun Canton <NA> CH.BE
## 24 <NA> Canton|Kanton|Chantun Canton <NA> CH.FR
## 25 <NA> Canton|Kanton|Chantun Canton <NA> CH.GE
## 26 <NA> Canton|Kanton|Chantun Canton <NA> CH.GL
## 2 <NA> Canton|Kanton|Chantun Canton <NA> CH.GR
## 3 <NA> Canton|Kanton|Chantun Canton <NA> CH.JU
## 4 <NA> Canton|Kanton|Chantun Canton <NA> CH.LU
## 5 <NA> Canton|Kanton|Chantun Canton <NA> CH.NE
## 6 <NA> Canton|Kanton|Chantun Canton <NA> CH.NW
## 7 <NA> Canton|Kanton|Chantun Canton <NA> CH.OW
## 8 <NA> Canton|Kanton|Chantun Canton <NA> CH.SG
## 9 <NA> Canton|Kanton|Chantun Canton <NA> CH.SH
## 10 <NA> Canton|Kanton|Chantun Canton <NA> CH.SZ
## 11 <NA> Canton|Kanton|Chantun Canton <NA> CH.SO
## 13 <NA> Canton|Kanton|Chantun Canton <NA> CH.TG
## 14 <NA> Canton|Kanton|Chantun Canton <NA> CH.TI
## 15 <NA> Canton|Kanton|Chantun Canton <NA> CH.UR
## 16 <NA> Canton|Kanton|Chantun Canton <NA> CH.VS
## 17 <NA> Canton|Kanton|Chantun Canton <NA> CH.VD
## 18 <NA> Canton|Kanton|Chantun Canton <NA> CH.ZG
## 19 <NA> Canton|Kanton|Chantun Canton <NA> CH.ZHJe crée désormais des données linsguistiques, à partir d’un sondage très rigoureux auprès de trois collègues sur l’emploi de quatre-vingts et huitante en Suisse.
#Je crée un vecteur avec les cantons
NAME_1<-c("Fribourg","Neuchâtel", "Genève", "Jura", "Valais", "Vaud",
"Aargau", "Appenzell Ausserrhoden", "Appenzell Innerrhoden", "Basel-Landschaft", "Basel-Stadt", "Bern", "Glarus", "Graubünden", "Lucerne", "Nidwalden", "Obwalden", "Sankt Gallen", "Schaffhausen", "Schwyz", "Solothurn", "Thurgau", "Ticino", "Uri", "Zug", "Zürich")
#Je crée un vecteur avec la manière de dire "80" pour chacun des cantons
eighty<-c("huitante","quatre", "quatre", "quatre", "huitante", "huitante",
"NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA", "NA","NA", "NA")
#Je transforme les deux vecteurs en un data frame
dialecto<-data.frame(NAME_1,eighty)
#J'affiche le data frame
dialecto## NAME_1 eighty
## 1 Fribourg huitante
## 2 Neuchâtel quatre
## 3 Genève quatre
## 4 Jura quatre
## 5 Valais huitante
## 6 Vaud huitante
## 7 Aargau NA
## 8 Appenzell Ausserrhoden NA
## 9 Appenzell Innerrhoden NA
## 10 Basel-Landschaft NA
## 11 Basel-Stadt NA
## 12 Bern NA
## 13 Glarus NA
## 14 Graubünden NA
## 15 Lucerne NA
## 16 Nidwalden NA
## 17 Obwalden NA
## 18 Sankt Gallen NA
## 19 Schaffhausen NA
## 20 Schwyz NA
## 21 Solothurn NA
## 22 Thurgau NA
## 23 Ticino NA
## 24 Uri NA
## 25 Zug NA
## 26 Zürich NAJe rajoute ces nouvelles informations à mon objet Switzerland1_UTM, que je convertis en data.frame.
#Je fusionne le data.frame téléchargé avec la carte, et celui que je viens de fabriquer. Pour cela j'utilise le nom des cantons comme clef.
Switzerland1_UTM@data <- join(Switzerland1_UTM@data, dialecto, by="NAME_1")
#Je transforme les données spatiales en data.frame avec la fonction fortify()
Switzerland1_df <- fortify(Switzerland1_UTM)## Regions defined for each Polygons## long lat order hole piece id group
## 1 7.949511 47.27751 1 FALSE 1 1 1.1
## 2 7.948970 47.27542 2 FALSE 1 1 1.1
## 3 7.949024 47.27444 3 FALSE 1 1 1.1
## 4 7.949273 47.27385 4 FALSE 1 1 1.1
## 5 7.950078 47.27288 5 FALSE 1 1 1.1
## 6 7.950778 47.27165 6 FALSE 1 1 1.1
## 7 7.951470 47.26959 7 FALSE 1 1 1.1
## 8 7.952139 47.26820 8 FALSE 1 1 1.1
## 9 7.952561 47.26752 9 FALSE 1 1 1.1
## 10 7.954035 47.26564 10 FALSE 1 1 1.1
## 11 7.954894 47.26489 11 FALSE 1 1 1.1
## 12 7.956356 47.26396 12 FALSE 1 1 1.1
## 13 7.957070 47.26315 13 FALSE 1 1 1.1
## 14 7.957511 47.26251 14 FALSE 1 1 1.1
## 15 7.958943 47.25986 15 FALSE 1 1 1.1
## 16 7.959839 47.25848 16 FALSE 1 1 1.1
## 17 7.960482 47.25677 17 FALSE 1 1 1.1
## 18 7.959226 47.25617 18 FALSE 1 1 1.1
## 19 7.958264 47.25546 19 FALSE 1 1 1.1
## 20 7.957337 47.25492 20 FALSE 1 1 1.1
## 21 7.956496 47.25432 21 FALSE 1 1 1.1
## 22 7.952929 47.25279 22 FALSE 1 1 1.1
## 23 7.952283 47.25242 23 FALSE 1 1 1.1
## 24 7.951153 47.25162 24 FALSE 1 1 1.1
## 25 7.950617 47.25116 25 FALSE 1 1 1.1
## 26 7.948427 47.24867 26 FALSE 1 1 1.1
## 27 7.946860 47.24743 27 FALSE 1 1 1.1
## 28 7.946014 47.24689 28 FALSE 1 1 1.1
## 29 7.945251 47.24630 29 FALSE 1 1 1.1
## 30 7.943157 47.24501 30 FALSE 1 1 1.1
## 31 7.940971 47.24411 31 FALSE 1 1 1.1
## 32 7.938853 47.24335 32 FALSE 1 1 1.1
## 33 7.935764 47.24191 33 FALSE 1 1 1.1
## 34 7.934894 47.24143 34 FALSE 1 1 1.1
## 35 7.933601 47.24041 35 FALSE 1 1 1.1
## 36 7.932611 47.24001 36 FALSE 1 1 1.1
## 37 7.931656 47.23984 37 FALSE 1 1 1.1
## 38 7.930643 47.23975 38 FALSE 1 1 1.1
## 39 7.925146 47.23968 39 FALSE 1 1 1.1
## 40 7.922497 47.24081 40 FALSE 1 1 1.1
## 41 7.918983 47.24276 41 FALSE 1 1 1.1
## 42 7.916769 47.24374 42 FALSE 1 1 1.1
## 43 7.912996 47.24589 43 FALSE 1 1 1.1
## 44 7.910844 47.24685 44 FALSE 1 1 1.1
## 45 7.909999 47.24704 45 FALSE 1 1 1.1
## 46 7.908596 47.24710 46 FALSE 1 1 1.1
## 47 7.905949 47.24687 47 FALSE 1 1 1.1
## 48 7.900452 47.24660 48 FALSE 1 1 1.1
## 49 7.899268 47.24646 49 FALSE 1 1 1.1
## 50 7.895161 47.24577 50 FALSE 1 1 1.1
## 51 7.891780 47.24559 51 FALSE 1 1 1.1
## 52 7.890963 47.24559 52 FALSE 1 1 1.1
## 53 7.889602 47.24498 53 FALSE 1 1 1.1
## 54 7.888649 47.24469 54 FALSE 1 1 1.1
## 55 7.880252 47.24296 55 FALSE 1 1 1.1
## 56 7.877781 47.24255 56 FALSE 1 1 1.1
## 57 7.875315 47.24192 57 FALSE 1 1 1.1
## 58 7.867531 47.24074 58 FALSE 1 1 1.1
## 59 7.862585 47.23914 59 FALSE 1 1 1.1
## 60 7.854717 47.23632 60 FALSE 1 1 1.1
## 61 7.853776 47.23606 61 FALSE 1 1 1.1
## 62 7.852850 47.23590 62 FALSE 1 1 1.1
## 63 7.851374 47.23583 63 FALSE 1 1 1.1
## 64 7.849473 47.23608 64 FALSE 1 1 1.1
## 65 7.844608 47.23729 65 FALSE 1 1 1.1
## 66 7.841947 47.23819 66 FALSE 1 1 1.1
## 67 7.840059 47.23950 67 FALSE 1 1 1.1
## 68 7.838386 47.24091 68 FALSE 1 1 1.1
## 69 7.837325 47.24199 69 FALSE 1 1 1.1
## 70 7.835357 47.24374 70 FALSE 1 1 1.1
## 71 7.834295 47.24481 71 FALSE 1 1 1.1
## 72 7.832361 47.24639 72 FALSE 1 1 1.1
## 73 7.831118 47.24723 73 FALSE 1 1 1.1
## 74 7.830626 47.24776 74 FALSE 1 1 1.1
## 75 7.829986 47.24869 75 FALSE 1 1 1.1
## 76 7.829260 47.25039 76 FALSE 1 1 1.1
## 77 7.829138 47.25110 77 FALSE 1 1 1.1
## 78 7.829155 47.25181 78 FALSE 1 1 1.1
## 79 7.829306 47.25250 79 FALSE 1 1 1.1
## 80 7.829866 47.25381 80 FALSE 1 1 1.1
## 81 7.830181 47.25505 81 FALSE 1 1 1.1
## 82 7.831082 47.25707 82 FALSE 1 1 1.1
## 83 7.831390 47.26022 83 FALSE 1 1 1.1
## 84 7.831359 47.26213 84 FALSE 1 1 1.1
## 85 7.831195 47.26354 85 FALSE 1 1 1.1
## 86 7.830186 47.26715 86 FALSE 1 1 1.1
## 87 7.828599 47.27051 87 FALSE 1 1 1.1
## 88 7.831689 47.27105 88 FALSE 1 1 1.1
## 89 7.833370 47.27153 89 FALSE 1 1 1.1
## 90 7.834134 47.27184 90 FALSE 1 1 1.1
## 91 7.835888 47.27293 91 FALSE 1 1 1.1
## 92 7.838246 47.27468 92 FALSE 1 1 1.1
## 93 7.840719 47.27635 93 FALSE 1 1 1.1
## 94 7.842224 47.27757 94 FALSE 1 1 1.1
## 95 7.843249 47.27914 95 FALSE 1 1 1.1
## 96 7.844139 47.28009 96 FALSE 1 1 1.1
## 97 7.844703 47.28112 97 FALSE 1 1 1.1
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## 1653 8.316434 47.23858 1653 FALSE 1 1 1.1
## 1654 8.316011 47.24081 1654 FALSE 1 1 1.1
## 1655 8.316051 47.24189 1655 FALSE 1 1 1.1
## 1656 8.316759 47.24524 1656 FALSE 1 1 1.1
## 1657 8.315650 47.24671 1657 FALSE 1 1 1.1
## 1658 8.314746 47.24776 1658 FALSE 1 1 1.1
## 1659 8.314443 47.24811 1659 FALSE 1 1 1.1
## 1660 8.313597 47.24933 1660 FALSE 1 1 1.1
## 1661 8.312264 47.25094 1661 FALSE 1 1 1.1
## 1662 8.311336 47.25178 1662 FALSE 1 1 1.1
## 1663 8.310405 47.25308 1663 FALSE 1 1 1.1
## 1664 8.309959 47.25340 1664 FALSE 1 1 1.1
## 1665 8.309190 47.25370 1665 FALSE 1 1 1.1
## 1666 8.308783 47.25472 1666 FALSE 1 1 1.1
## 1667 8.308364 47.25541 1667 FALSE 1 1 1.1
## 1668 8.305096 47.25655 1668 FALSE 1 1 1.1
## 1669 8.301506 47.25911 1669 FALSE 1 1 1.1
## 1670 8.300566 47.26019 1670 FALSE 1 1 1.1
## 1671 8.299566 47.26102 1671 FALSE 1 1 1.1
## 1672 8.298099 47.26292 1672 FALSE 1 1 1.1
## 1673 8.297839 47.26350 1673 FALSE 1 1 1.1
## 1674 8.296998 47.26604 1674 FALSE 1 1 1.1
## 1675 8.296106 47.26924 1675 FALSE 1 1 1.1
## 1676 8.296082 47.27051 1676 FALSE 1 1 1.1
## 1677 8.296225 47.27110 1677 FALSE 1 1 1.1
## 1678 8.296530 47.27161 1678 FALSE 1 1 1.1
## 1679 8.298053 47.27283 1679 FALSE 1 1 1.1
## 1680 8.298943 47.27333 1680 FALSE 1 1 1.1
## 1681 8.300312 47.27428 1681 FALSE 1 1 1.1
## 1682 8.300656 47.27461 1682 FALSE 1 1 1.1
## 1683 8.300926 47.27520 1683 FALSE 1 1 1.1
## 1684 8.300900 47.27580 1684 FALSE 1 1 1.1
## 1685 8.300608 47.27617 1685 FALSE 1 1 1.1
## 1686 8.300143 47.27645 1686 FALSE 1 1 1.1
## 1687 8.298440 47.27706 1687 FALSE 1 1 1.1
## 1688 8.296458 47.27791 1688 FALSE 1 1 1.1
## 1689 8.295606 47.27850 1689 FALSE 1 1 1.1
## 1690 8.294711 47.27903 1690 FALSE 1 1 1.1
## 1691 8.293956 47.27963 1691 FALSE 1 1 1.1
## 1692 8.293099 47.28017 1692 FALSE 1 1 1.1
## 1693 8.292361 47.28080 1693 FALSE 1 1 1.1
## 1694 8.291508 47.28138 1694 FALSE 1 1 1.1
## 1695 8.290505 47.28233 1695 FALSE 1 1 1.1
## 1696 8.289922 47.28274 1696 FALSE 1 1 1.1
## 1697 8.289248 47.28309 1697 FALSE 1 1 1.1
## 1698 8.285260 47.28465 1698 FALSE 1 1 1.1
## 1699 8.283758 47.28542 1699 FALSE 1 1 1.1
## 1700 8.282079 47.28587 1700 FALSE 1 1 1.1
## 1701 8.280800 47.28640 1701 FALSE 1 1 1.1
## 1702 8.279772 47.28656 1702 FALSE 1 1 1.1
## 1703 8.278952 47.28654 1703 FALSE 1 1 1.1
## 1704 8.278124 47.28643 1704 FALSE 1 1 1.1
## 1705 8.276637 47.28602 1705 FALSE 1 1 1.1
## 1706 8.275771 47.28585 1706 FALSE 1 1 1.1
## 1707 8.274786 47.28580 1707 FALSE 1 1 1.1
## 1708 8.273809 47.28590 1708 FALSE 1 1 1.1
## 1709 8.273345 47.28600 1709 FALSE 1 1 1.1
## 1710 8.272639 47.28628 1710 FALSE 1 1 1.1
## 1711 8.270902 47.28738 1711 FALSE 1 1 1.1
## 1712 8.270108 47.28761 1712 FALSE 1 1 1.1
## 1713 8.268747 47.28770 1713 FALSE 1 1 1.1
## 1714 8.267319 47.28758 1714 FALSE 1 1 1.1
## 1715 8.266363 47.28757 1715 FALSE 1 1 1.1
## 1716 8.265539 47.28766 1716 FALSE 1 1 1.1
## 1717 8.263951 47.28802 1717 FALSE 1 1 1.1
## 1718 8.263130 47.28833 1718 FALSE 1 1 1.1
## 1719 8.262458 47.28870 1719 FALSE 1 1 1.1
## 1720 8.261869 47.28913 1720 FALSE 1 1 1.1
## 1721 8.260729 47.29015 1721 FALSE 1 1 1.1
## 1722 8.257566 47.29070 1722 FALSE 1 1 1.1
## 1723 8.255450 47.29081 1723 FALSE 1 1 1.1
## 1724 8.254425 47.29075 1724 FALSE 1 1 1.1
## 1725 8.253452 47.29058 1725 FALSE 1 1 1.1
## 1726 8.252705 47.29029 1726 FALSE 1 1 1.1
## 1727 8.252206 47.28995 1727 FALSE 1 1 1.1
## 1728 8.251700 47.28939 1728 FALSE 1 1 1.1
## 1729 8.250760 47.28808 1729 FALSE 1 1 1.1
## 1730 8.250084 47.28657 1730 FALSE 1 1 1.1
## 1731 8.249725 47.28603 1731 FALSE 1 1 1.1
## 1732 8.248113 47.28488 1732 FALSE 1 1 1.1
## 1733 8.246300 47.28427 1733 FALSE 1 1 1.1
## 1734 8.245639 47.28389 1734 FALSE 1 1 1.1
## 1735 8.244698 47.28292 1735 FALSE 1 1 1.1
## 1736 8.244138 47.28213 1736 FALSE 1 1 1.1
## 1737 8.243813 47.28104 1737 FALSE 1 1 1.1
## 1738 8.243744 47.27989 1738 FALSE 1 1 1.1
## 1739 8.243841 47.27834 1739 FALSE 1 1 1.1
## 1740 8.244173 47.27610 1740 FALSE 1 1 1.1
## 1741 8.241735 47.27625 1741 FALSE 1 1 1.1
## 1742 8.239184 47.27618 1742 FALSE 1 1 1.1
## 1743 8.238239 47.27627 1743 FALSE 1 1 1.1
## 1744 8.236509 47.27672 1744 FALSE 1 1 1.1
## 1745 8.234862 47.27747 1745 FALSE 1 1 1.1
## 1746 8.233473 47.27775 1746 FALSE 1 1 1.1
## 1747 8.232846 47.27776 1747 FALSE 1 1 1.1
## 1748 8.231775 47.27761 1748 FALSE 1 1 1.1
## 1749 8.230871 47.27741 1749 FALSE 1 1 1.1
## 1750 8.229957 47.27710 1750 FALSE 1 1 1.1
## 1751 8.229671 47.27481 1751 FALSE 1 1 1.1
## 1752 8.229170 47.27311 1752 FALSE 1 1 1.1
## 1753 8.231398 47.27193 1753 FALSE 1 1 1.1
## 1754 8.231887 47.27163 1754 FALSE 1 1 1.1
## 1755 8.232283 47.27119 1755 FALSE 1 1 1.1
## 1756 8.232989 47.27005 1756 FALSE 1 1 1.1
## 1757 8.233657 47.26938 1757 FALSE 1 1 1.1
## 1758 8.233937 47.26887 1758 FALSE 1 1 1.1
## 1759 8.234139 47.26797 1759 FALSE 1 1 1.1
## 1760 8.234203 47.26701 1760 FALSE 1 1 1.1
## 1761 8.234155 47.26569 1761 FALSE 1 1 1.1
## 1762 8.233970 47.26472 1762 FALSE 1 1 1.1
## 1763 8.233702 47.26411 1763 FALSE 1 1 1.1
## 1764 8.233289 47.26356 1764 FALSE 1 1 1.1
## 1765 8.231788 47.26208 1765 FALSE 1 1 1.1
## 1766 8.231395 47.26155 1766 FALSE 1 1 1.1
## 1767 8.231137 47.26094 1767 FALSE 1 1 1.1
## 1768 8.230760 47.25944 1768 FALSE 1 1 1.1
## 1769 8.230489 47.25889 1769 FALSE 1 1 1.1
## 1770 8.230279 47.25865 1770 FALSE 1 1 1.1
## 1771 8.229813 47.25832 1771 FALSE 1 1 1.1
## 1772 8.227646 47.25714 1772 FALSE 1 1 1.1
## 1773 8.228077 47.25555 1773 FALSE 1 1 1.1
## 1774 8.229308 47.25258 1774 FALSE 1 1 1.1
## 1775 8.230133 47.25087 1775 FALSE 1 1 1.1
## 1776 8.229070 47.24978 1776 FALSE 1 1 1.1
## 1777 8.228492 47.24950 1777 FALSE 1 1 1.1
## 1778 8.228127 47.24940 1778 FALSE 1 1 1.1
## 1779 8.227348 47.24934 1779 FALSE 1 1 1.1
## 1780 8.226547 47.24939 1780 FALSE 1 1 1.1
## 1781 8.225250 47.24973 1781 FALSE 1 1 1.1
## 1782 8.225034 47.24968 1782 FALSE 1 1 1.1
## 1783 8.224007 47.25079 1783 FALSE 1 1 1.1
## 1784 8.220371 47.25569 1784 FALSE 1 1 1.1
## 1785 8.220321 47.25544 1785 FALSE 1 1 1.1
## 1786 8.218708 47.25475 1786 FALSE 1 1 1.1
## 1787 8.217418 47.25401 1787 FALSE 1 1 1.1
## 1788 8.213855 47.25245 1788 FALSE 1 1 1.1
## 1789 8.212170 47.25160 1789 FALSE 1 1 1.1
## 1790 8.210389 47.25110 1790 FALSE 1 1 1.1
## 1791 8.208940 47.25088 1791 FALSE 1 1 1.1
## 1792 8.208659 47.24944 1792 FALSE 1 1 1.1
## 1793 8.208377 47.24876 1793 FALSE 1 1 1.1
## 1794 8.206899 47.24688 1794 FALSE 1 1 1.1
## 1795 8.206592 47.24625 1795 FALSE 1 1 1.1
## 1796 8.206548 47.24561 1796 FALSE 1 1 1.1
## 1797 8.206713 47.24368 1797 FALSE 1 1 1.1
## 1798 8.206406 47.24123 1798 FALSE 1 1 1.1
## 1799 8.206039 47.24019 1799 FALSE 1 1 1.1
## 1800 8.204621 47.23820 1800 FALSE 1 1 1.1
## 1801 8.204362 47.23751 1801 FALSE 1 1 1.1
## 1802 8.204245 47.23679 1802 FALSE 1 1 1.1
## 1803 8.204276 47.23570 1803 FALSE 1 1 1.1
## 1804 8.204435 47.23499 1804 FALSE 1 1 1.1
## 1805 8.205556 47.23220 1805 FALSE 1 1 1.1
## 1806 8.206679 47.22882 1806 FALSE 1 1 1.1
## 1807 8.203359 47.22914 1807 FALSE 1 1 1.1
## 1808 8.200510 47.22921 1808 FALSE 1 1 1.1
## 1809 8.198288 47.22910 1809 FALSE 1 1 1.1
## 1810 8.195789 47.22869 1810 FALSE 1 1 1.1
## 1811 8.194893 47.22852 1811 FALSE 1 1 1.1
## 1812 8.194106 47.22826 1812 FALSE 1 1 1.1
## 1813 8.191125 47.22655 1813 FALSE 1 1 1.1
## 1814 8.189260 47.22598 1814 FALSE 1 1 1.1
## 1815 8.188265 47.22554 1815 FALSE 1 1 1.1
## 1816 8.186968 47.22528 1816 FALSE 1 1 1.1
## 1817 8.185508 47.22522 1817 FALSE 1 1 1.1
## 1818 8.183058 47.22531 1818 FALSE 1 1 1.1
## 1819 8.180576 47.22531 1819 FALSE 1 1 1.1
## 1820 8.179366 47.22541 1820 FALSE 1 1 1.1
## 1821 8.177201 47.22573 1821 FALSE 1 1 1.1
## 1822 8.176847 47.22606 1822 FALSE 1 1 1.1
## 1823 8.176031 47.22659 1823 FALSE 1 1 1.1
## 1824 8.174487 47.22783 1824 FALSE 1 1 1.1
## 1825 8.173955 47.22857 1825 FALSE 1 1 1.1
## 1826 8.173431 47.22964 1826 FALSE 1 1 1.1
## 1827 8.166704 47.23217 1827 FALSE 1 1 1.1
## 1828 8.164503 47.23342 1828 FALSE 1 1 1.1
## 1829 8.163146 47.23440 1829 FALSE 1 1 1.1
## 1830 8.162385 47.23518 1830 FALSE 1 1 1.1
## 1831 8.162127 47.23592 1831 FALSE 1 1 1.1
## 1832 8.162126 47.23669 1832 FALSE 1 1 1.1
## 1833 8.162537 47.23828 1833 FALSE 1 1 1.1
## 1834 8.162310 47.23920 1834 FALSE 1 1 1.1
## 1835 8.161937 47.23978 1835 FALSE 1 1 1.1
## 1836 8.161289 47.24029 1836 FALSE 1 1 1.1
## 1837 8.158613 47.24142 1837 FALSE 1 1 1.1
## 1838 8.156751 47.24243 1838 FALSE 1 1 1.1
## 1839 8.160261 47.24327 1839 FALSE 1 1 1.1
## 1840 8.163480 47.24385 1840 FALSE 1 1 1.1
## 1841 8.167593 47.24425 1841 FALSE 1 1 1.1
## 1842 8.169065 47.24447 1842 FALSE 1 1 1.1
## 1843 8.170403 47.24459 1843 FALSE 1 1 1.1
## 1844 8.170981 47.24459 1844 FALSE 1 1 1.1
## 1845 8.171527 47.24451 1845 FALSE 1 1 1.1
## 1846 8.172903 47.24390 1846 FALSE 1 1 1.1
## 1847 8.173694 47.24369 1847 FALSE 1 1 1.1
## 1848 8.175118 47.24354 1848 FALSE 1 1 1.1
## 1849 8.177197 47.24347 1849 FALSE 1 1 1.1
## 1850 8.177590 47.24449 1850 FALSE 1 1 1.1
## 1851 8.178000 47.24518 1851 FALSE 1 1 1.1
## 1852 8.179189 47.24673 1852 FALSE 1 1 1.1
## 1853 8.179706 47.24720 1853 FALSE 1 1 1.1
## 1854 8.180673 47.24777 1854 FALSE 1 1 1.1
## 1855 8.182005 47.24826 1855 FALSE 1 1 1.1
## 1856 8.181797 47.24999 1856 FALSE 1 1 1.1
## 1857 8.181141 47.25276 1857 FALSE 1 1 1.1
## 1858 8.180860 47.25346 1858 FALSE 1 1 1.1
## 1859 8.180322 47.25446 1859 FALSE 1 1 1.1
## 1860 8.179780 47.25508 1860 FALSE 1 1 1.1
## 1861 8.179021 47.25560 1861 FALSE 1 1 1.1
## 1862 8.178126 47.25596 1862 FALSE 1 1 1.1
## 1863 8.176745 47.25624 1863 FALSE 1 1 1.1
## 1864 8.175753 47.25626 1864 FALSE 1 1 1.1
## 1865 8.173510 47.25595 1865 FALSE 1 1 1.1
## 1866 8.172728 47.25569 1866 FALSE 1 1 1.1
## 1867 8.170362 47.25434 1867 FALSE 1 1 1.1
## 1868 8.169660 47.25404 1868 FALSE 1 1 1.1
## 1869 8.168800 47.25386 1869 FALSE 1 1 1.1
## 1870 8.165900 47.25348 1870 FALSE 1 1 1.1
## 1871 8.163455 47.25292 1871 FALSE 1 1 1.1
## 1872 8.161479 47.25236 1872 FALSE 1 1 1.1
## 1873 8.160400 47.25219 1873 FALSE 1 1 1.1
## 1874 8.159281 47.25210 1874 FALSE 1 1 1.1
## 1875 8.154170 47.25190 1875 FALSE 1 1 1.1
## 1876 8.150079 47.25129 1876 FALSE 1 1 1.1
## 1877 8.148614 47.25091 1877 FALSE 1 1 1.1
## 1878 8.144538 47.25091 1878 FALSE 1 1 1.1
## 1879 8.142220 47.25071 1879 FALSE 1 1 1.1
## 1880 8.138649 47.25022 1880 FALSE 1 1 1.1
## 1881 8.137639 47.25002 1881 FALSE 1 1 1.1
## 1882 8.135333 47.24939 1882 FALSE 1 1 1.1
## 1883 8.132508 47.24823 1883 FALSE 1 1 1.1
## 1884 8.131425 47.24729 1884 FALSE 1 1 1.1
## 1885 8.130350 47.24665 1885 FALSE 1 1 1.1
## 1886 8.129501 47.24605 1886 FALSE 1 1 1.1
## 1887 8.127617 47.24512 1887 FALSE 1 1 1.1
## 1888 8.125720 47.24439 1888 FALSE 1 1 1.1
## 1889 8.124935 47.24524 1889 FALSE 1 1 1.1
## 1890 8.124502 47.24558 1890 FALSE 1 1 1.1
## 1891 8.123171 47.24645 1891 FALSE 1 1 1.1
## 1892 8.122238 47.24681 1892 FALSE 1 1 1.1
## 1893 8.118746 47.24737 1893 FALSE 1 1 1.1
## 1894 8.112836 47.24764 1894 FALSE 1 1 1.1
## 1895 8.109638 47.24807 1895 FALSE 1 1 1.1
## 1896 8.108435 47.24974 1896 FALSE 1 1 1.1
## 1897 8.107502 47.25068 1897 FALSE 1 1 1.1
## 1898 8.107040 47.25151 1898 FALSE 1 1 1.1
## 1899 8.106863 47.25215 1899 FALSE 1 1 1.1
## 1900 8.106914 47.25448 1900 FALSE 1 1 1.1
## 1901 8.106650 47.25544 1901 FALSE 1 1 1.1
## 1902 8.106234 47.25594 1902 FALSE 1 1 1.1
## 1903 8.105014 47.25699 1903 FALSE 1 1 1.1
## 1904 8.103912 47.25782 1904 FALSE 1 1 1.1
## 1905 8.102198 47.25966 1905 FALSE 1 1 1.1
## 1906 8.101778 47.26022 1906 FALSE 1 1 1.1
## 1907 8.100996 47.26182 1907 FALSE 1 1 1.1
## 1908 8.100576 47.26231 1908 FALSE 1 1 1.1
## 1909 8.099713 47.26286 1909 FALSE 1 1 1.1
## 1910 8.097794 47.26360 1910 FALSE 1 1 1.1
## 1911 8.096535 47.26395 1911 FALSE 1 1 1.1
## 1912 8.094572 47.26429 1912 FALSE 1 1 1.1
## 1913 8.093691 47.26451 1913 FALSE 1 1 1.1
## 1914 8.090784 47.26550 1914 FALSE 1 1 1.1
## 1915 8.088993 47.26639 1915 FALSE 1 1 1.1
## 1916 8.087335 47.26532 1916 FALSE 1 1 1.1
## 1917 8.085995 47.26463 1917 FALSE 1 1 1.1
## 1918 8.085043 47.26432 1918 FALSE 1 1 1.1
## 1919 8.083117 47.26392 1919 FALSE 1 1 1.1
## 1920 8.082160 47.26366 1920 FALSE 1 1 1.1
## 1921 8.079535 47.26266 1921 FALSE 1 1 1.1
## 1922 8.078811 47.26219 1922 FALSE 1 1 1.1
## 1923 8.077716 47.26120 1923 FALSE 1 1 1.1
## 1924 8.074237 47.25887 1924 FALSE 1 1 1.1
## 1925 8.073156 47.25837 1925 FALSE 1 1 1.1
## 1926 8.071779 47.25801 1926 FALSE 1 1 1.1
## 1927 8.070784 47.25787 1927 FALSE 1 1 1.1
## 1928 8.069753 47.25783 1928 FALSE 1 1 1.1
## 1929 8.067704 47.25796 1929 FALSE 1 1 1.1
## 1930 8.065842 47.25842 1930 FALSE 1 1 1.1
## 1931 8.065065 47.25868 1931 FALSE 1 1 1.1
## 1932 8.064055 47.25915 1932 FALSE 1 1 1.1
## 1933 8.063230 47.25936 1933 FALSE 1 1 1.1
## 1934 8.061797 47.25953 1934 FALSE 1 1 1.1
## 1935 8.058584 47.25971 1935 FALSE 1 1 1.1
## 1936 8.056038 47.25995 1936 FALSE 1 1 1.1
## 1937 8.056789 47.25802 1937 FALSE 1 1 1.1
## 1938 8.057434 47.25598 1938 FALSE 1 1 1.1
## 1939 8.058314 47.25444 1939 FALSE 1 1 1.1
## 1940 8.060101 47.25293 1940 FALSE 1 1 1.1
## 1941 8.062672 47.25108 1941 FALSE 1 1 1.1
## 1942 8.060305 47.24955 1942 FALSE 1 1 1.1
## 1943 8.058478 47.24854 1943 FALSE 1 1 1.1
## 1944 8.057561 47.24817 1944 FALSE 1 1 1.1
## 1945 8.054721 47.24729 1945 FALSE 1 1 1.1
## 1946 8.053724 47.24715 1946 FALSE 1 1 1.1
## 1947 8.052710 47.24713 1947 FALSE 1 1 1.1
## 1948 8.049681 47.24739 1948 FALSE 1 1 1.1
## 1949 8.048152 47.24738 1949 FALSE 1 1 1.1
## 1950 8.046091 47.24716 1950 FALSE 1 1 1.1
## 1951 8.042458 47.24688 1951 FALSE 1 1 1.1
## 1952 8.040376 47.24661 1952 FALSE 1 1 1.1
## 1953 8.036217 47.24627 1953 FALSE 1 1 1.1
## 1954 8.033232 47.24575 1954 FALSE 1 1 1.1
## 1955 8.032331 47.24549 1955 FALSE 1 1 1.1
## 1956 8.031181 47.24486 1956 FALSE 1 1 1.1
## 1957 8.030132 47.24398 1957 FALSE 1 1 1.1
## 1958 8.029385 47.24368 1958 FALSE 1 1 1.1
## 1959 8.028950 47.24363 1959 FALSE 1 1 1.1
## 1960 8.028086 47.24368 1960 FALSE 1 1 1.1
## 1961 8.027203 47.24384 1961 FALSE 1 1 1.1
## 1962 8.022138 47.24563 1962 FALSE 1 1 1.1
## 1963 8.021187 47.24588 1963 FALSE 1 1 1.1
## 1964 8.019222 47.24630 1964 FALSE 1 1 1.1
## 1965 8.017368 47.24648 1965 FALSE 1 1 1.1
## 1966 8.016773 47.24806 1966 FALSE 1 1 1.1
## 1967 8.016500 47.25084 1967 FALSE 1 1 1.1
## 1968 8.016563 47.25555 1968 FALSE 1 1 1.1
## 1969 8.017024 47.25898 1969 FALSE 1 1 1.1
## 1970 8.017011 47.25984 1970 FALSE 1 1 1.1
## 1971 8.016832 47.26070 1971 FALSE 1 1 1.1
## 1972 8.016530 47.26144 1972 FALSE 1 1 1.1
## 1973 8.014978 47.26426 1973 FALSE 1 1 1.1
## 1974 8.011710 47.26703 1974 FALSE 1 1 1.1
## 1975 8.010270 47.26847 1975 FALSE 1 1 1.1
## 1976 8.009421 47.26906 1976 FALSE 1 1 1.1
## 1977 8.007922 47.27029 1977 FALSE 1 1 1.1
## 1978 8.007593 47.27073 1978 FALSE 1 1 1.1
## 1979 8.006923 47.27188 1979 FALSE 1 1 1.1
## 1980 8.006058 47.27282 1980 FALSE 1 1 1.1
## 1981 8.005785 47.27335 1981 FALSE 1 1 1.1
## 1982 8.005361 47.27449 1982 FALSE 1 1 1.1
## 1983 8.004743 47.27614 1983 FALSE 1 1 1.1
## 1984 8.003896 47.27747 1984 FALSE 1 1 1.1
## 1985 8.002740 47.27850 1985 FALSE 1 1 1.1
## 1986 8.000623 47.28090 1986 FALSE 1 1 1.1
## 1987 7.999584 47.28167 1987 FALSE 1 1 1.1
## 1988 7.998082 47.28291 1988 FALSE 1 1 1.1
## 1989 7.997576 47.28370 1989 FALSE 1 1 1.1
## 1990 7.996109 47.28398 1990 FALSE 1 1 1.1
## 1991 7.995127 47.28428 1991 FALSE 1 1 1.1
## 1992 7.993214 47.28341 1992 FALSE 1 1 1.1
## 1993 7.991477 47.28229 1993 FALSE 1 1 1.1
## 1994 7.990708 47.28169 1994 FALSE 1 1 1.1
## 1995 7.988636 47.28039 1995 FALSE 1 1 1.1
## 1996 7.986521 47.27958 1996 FALSE 1 1 1.1
## 1997 7.985042 47.27925 1997 FALSE 1 1 1.1
## 1998 7.981949 47.27896 1998 FALSE 1 1 1.1
## 1999 7.979337 47.27864 1999 FALSE 1 1 1.1
## 2000 7.974726 47.27824 2000 FALSE 1 1 1.1
## 2001 7.973768 47.27821 2001 FALSE 1 1 1.1
## 2002 7.972422 47.27834 2002 FALSE 1 1 1.1
## 2003 7.970648 47.27901 2003 FALSE 1 1 1.1
## 2004 7.969367 47.27937 2004 FALSE 1 1 1.1
## 2005 7.968362 47.27958 2005 FALSE 1 1 1.1
## 2006 7.967268 47.27970 2006 FALSE 1 1 1.1
## 2007 7.963204 47.27981 2007 FALSE 1 1 1.1
## 2008 7.958473 47.27982 2008 FALSE 1 1 1.1
## 2009 7.956189 47.27977 2009 FALSE 1 1 1.1
## 2010 7.954610 47.27959 2010 FALSE 1 1 1.1
## 2011 7.952942 47.27916 2011 FALSE 1 1 1.1
## 2012 7.952363 47.27893 2012 FALSE 1 1 1.1
## 2013 7.951177 47.27825 2013 FALSE 1 1 1.1
## 2014 7.949511 47.27751 2014 FALSE 1 1 1.1
## 2015 9.207076 47.27694 1 FALSE 1 12 12.1
## 2016 9.208278 47.27905 2 FALSE 1 12 12.1
## 2017 9.209665 47.28085 3 FALSE 1 12 12.1
## 2018 9.211722 47.28268 4 FALSE 1 12 12.1
## 2019 9.212301 47.28307 5 FALSE 1 12 12.1
## 2020 9.212963 47.28341 6 FALSE 1 12 12.1
## 2021 9.216947 47.28489 7 FALSE 1 12 12.1
## 2022 9.218032 47.28539 8 FALSE 1 12 12.1
## 2023 9.218611 47.28578 9 FALSE 1 12 12.1
## 2024 9.219630 47.28669 10 FALSE 1 12 12.1
## 2025 9.220460 47.28727 11 FALSE 1 12 12.1
## 2026 9.221674 47.28830 12 FALSE 1 12 12.1
## 2027 9.221919 47.28852 13 FALSE 1 12 12.1
## 2028 9.222295 47.28907 14 FALSE 1 12 12.1
## 2029 9.222877 47.29031 15 FALSE 1 12 12.1
## 2030 9.223543 47.29137 16 FALSE 1 12 12.1
## 2031 9.223829 47.29201 17 FALSE 1 12 12.1
## 2032 9.223999 47.29272 18 FALSE 1 12 12.1
## 2033 9.224071 47.29381 19 FALSE 1 12 12.1
## 2034 9.223951 47.29485 20 FALSE 1 12 12.1
## 2035 9.223714 47.29547 21 FALSE 1 12 12.1
## 2036 9.223284 47.29597 22 FALSE 1 12 12.1
## 2037 9.221348 47.29757 23 FALSE 1 12 12.1
## 2038 9.220336 47.29910 24 FALSE 1 12 12.1
## 2039 9.219691 47.29977 25 FALSE 1 12 12.1
## 2040 9.219368 47.30033 26 FALSE 1 12 12.1
## 2041 9.218685 47.30234 27 FALSE 1 12 12.1
## 2042 9.218458 47.30373 28 FALSE 1 12 12.1
## 2043 9.218424 47.30553 29 FALSE 1 12 12.1
## 2044 9.218527 47.30735 30 FALSE 1 12 12.1
## 2045 9.218809 47.30930 31 FALSE 1 12 12.1
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## 14284 10.049205 46.53622 381 FALSE 1 2 2.1
## 14285 10.049365 46.53592 382 FALSE 1 2 2.1
## [ reached 'max' / getOption("max.print") -- omitted 33265 rows ]Je peux créer des jeux de données restreints, que j’extraie de l’objet Switzerland1_UTM
#Je peux extraire un canton en fonction de son nom: ici celui de Genève
Geneve<-Switzerland1_UTM[Switzerland1_UTM@data$NAME_1 == "Genève",]
Geneve_df<-fortify(Geneve)## Regions defined for each Polygons#Je peux extraire plusieurs cantons en fonction d'un autre critère que son nom — ici, "huitante" pour "quatre-vingt"
canton_huitante<-Switzerland1_UTM[Switzerland1_UTM@data$eighty == "huitante",]
canton_huitante_df<-fortify(canton_huitante)## Regions defined for each Polygons#Je peux extraire plusieurs cantons en fonction d'un autre critère que son nom — ici, "quatre-vingt" pour "huitante"
canton_quatre<-Switzerland1_UTM[Switzerland1_UTM@data$eighty == "quatre",]
canton_quatre_df<-fortify(canton_quatre)## Regions defined for each PolygonsJe projette ensuite ces informations sur une carte, que j’appelle avec la fonction ggplot()
#La carte finale est une superposition de trois couches
ggplot() +
#Je commence par tous les cantons, en blanc
geom_polygon(data=Switzerland1_df, aes(long,lat, group=group), fill="white")
#La carte finale est une superposition de trois couches
ggplot() +
#Je commence par tous les cantons, en blanc
geom_polygon(data=Switzerland1_df, aes(long,lat, group=group), fill="white") +
#Je superpose le canton de Genève, en rouge
geom_polygon(data=Geneve_df, aes(long,lat,group=group), fill="red")
#La carte finale est une superposition de trois couches
ggplot() +
#Je commence par tous les cantons, en blanc
geom_polygon(data=Switzerland1_df, aes(long,lat, group=group), fill="white", size=0.1) +
#Je superpose le canton de Genève, en rouge
geom_polygon(data=Geneve_df, aes(long,lat,group=group), fill="red")+
#Je superpose enfin les contours des différents cantons
geom_path(data=Switzerland1_df, aes(long,lat, group=group), color="darkgrey", size=0.1)
Je peux placer plusieurs données différentes:
ggplot() +
#je place mes cantons où l'on dit "huitante"
geom_polygon(data = canton_huitante_df, aes(x = long, y = lat, group = group, fill = "huitante"), color = "black", size = 0.25) +
#je place mes cantons où l'on dit "quatre-vingts"
geom_polygon(data = canton_quatre_df, aes(x = long, y = lat, group = group, fill = "quatre-vingts")) +
scale_fill_manual(values=c("red", "white")) #scale_fill_brewer(palette = "Blues")
3.2 Des points
Je vais pouvoir rajouter des données scientifiques à mon enquête. En linguistique, on utilise les points géographiques de l’ALF (Atlas linguistique de la France), publié entre 1902 et 1910.
